Data warehouse risk management – risk identification
Data warehouse projects are highly complex, and as such, are inherently risky. It is the responsibility of the project manager to lead the data warehouse team in identifying all risks associated with a particular data warehouse implementation. The goal of this process is to document all essential information relating to project risk.
For additional information relating to risk, refer to the Project Diva article on project risk identification and management.
Risk ID |
Category |
Description |
Conditions |
Impact Description |
Possible mitigation |
1 |
Overall |
Loss of project sponsor |
Impending reorganization |
Scope, budget, staffing, schedule issues |
Identify secondary sponsor. Review project requirements with |
2 |
Overall |
General lack of experience with toolsets, methods and best |
Potential for huge margins of error in budgeting and scheduling; |
Strong resource management plan; clearly defined project |
|
3 |
Scope |
Wide range of users driving system requirements |
Conflicting user requirements |
Scope creep; application not meet user requirements; marginalized |
Ensure high level of user involvement; requirement |
4 |
Scope |
Changing system requirements |
Impending reorganization; new product development; staff |
Schedule delays; system not meet business |
Change management process; project sponsor |
5 |
Budget |
Inadequate Budget |
Project delay; scope scaled back; not meet business |
Research; professional cost estimation; contingency budgeting; sponsor |
|
6 |
Schedule |
Unrealistic schedule due to initial estimates / poorly understood |
Large initial delays; evidence of tasks excluded from |
Project delay; quality issues due to rushed delivery or exclusion of |
Research; sponsor support; professional |
7 |
ROI |
Disconnect between business objectives and project |
Changing business objectives; change in management; market |
Product not fit for use; no ROI |
Proactive alignment of project with business objectives; active |
8 |
Technical |
Scalability issues due to huge amounts of data, changing |
Poor system performance |
Data access issues |
Estimating toolsets; technical design completed by experienced |
9 |
Technical |
Support issues; heterogeneous environment; new |
Staff turnover; staff currently not trained on |
System unavailable |
Administrator training; staff training budget; DR |
10 |
Technical |
Poor data quality |
Useless datasets; not meet business requirements; system not |
Data quality review sub project; data cleansing; implement known clean |
|
11 |
Technical |
End user technical skills too low |
Implement canned reports and templates; user involvement in front end |
||
12 |
Implementation |
Vendor issues |
Budget; project delays |
Verify vendor financial viability; vendor |
|
13 |
Implementation |
User rejection |
System not used; users express dissatisfaction during training, |
High level of user involvement; prototyping; user feedback incorporated |
Data Warehouse Risk Identification (.doc format)